Paper Citation Record · LEDGER

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models

As of 11 August 2026, this Paper Citation Record lists 25 of 25 outbound references and 0 inbound Pith citation observations for arXiv:2607.14630.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2607.14630 v1

Coverage vector

measured 25 of 25 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-02T01:35:44.824860Z

measured 25 of 25 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

25 of 25 outbound references displayed

verified exact0
verified fuzzy0
unresolved25
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 8fdad455-67f5-42f6-a2bd-763dbf912e76 · outbound

This paper cites GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models GPTQ: Accurate Post-Training Quantization for Generative Pre-trained Transformers

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:42.139308Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0c15cf56-3e34-4926-9c87-5a6d479e6562 · outbound

This paper cites BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models BRECQ: Pushing the Limit of Post-Training Quantization by Block Reconstruction

Reference 2

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:42.201937Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 0a30f443-eb52-41a4-9ed9-093397107d49 · outbound

This paper cites Up or Down? Adaptive Rounding for Post-Training Quantization.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models Up or Down? Adaptive Rounding for Post-Training Quantization

Reference 3

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:42.260594Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 66ac6acf-9587-4ed4-8c7e-f6fc8f86cdf2 · outbound

This paper cites an unresolved cited work.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models Unresolved cited work

Reference 4

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:42.475221Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 65a9b4a5-5f7b-41bc-b16e-f4ad351355fe · outbound

This paper cites Model-Preserving Adaptive Rounding.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models Model-Preserving Adaptive Rounding

Reference 5

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:42.511516Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f65bd00c-5c75-46d4-887e-696f4adce7a8 · outbound

This paper cites Kim et al.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models Kim et al

Reference 6

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:42.620141Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 4e08ee3e-3c65-4757-a31f-f4681386b92b · outbound

This paper cites Rethinking Residual Errors in Compensation-based LLM Quantization.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models Rethinking Residual Errors in Compensation-based LLM Quantization

Reference 7

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:42.742492Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e4eb882a-a147-4cac-9d50-7907714d7f01 · outbound

This paper cites BinaryConnect: Training Deep Neural Networks with binary weights during propagations.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models BinaryConnect: Training Deep Neural Networks with binary weights during propagations

Reference 8

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:42.874126Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation acdd4316-b7e3-4779-8521-733465d47e84 · outbound

This paper cites XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models XNOR-Net: ImageNet Classification Using Binary Convolutional Neural Networks

Reference 9

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:43.010828Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3d625a9-23ef-48cf-93ad-4ed24f282cd6 · outbound

This paper cites BitNet: Scaling 1-bit Transformers for Large Language Models.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models BitNet: Scaling 1-bit Transformers for Large Language Models

Reference 10

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:43.133161Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation ba88d05a-0909-488b-aece-4d58cc9356ac · outbound

This paper cites The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models The Era of 1-bit LLMs: All Large Language Models are in 1.58 Bits

Reference 11

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:43.203227Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d615411e-6a98-4668-9e91-ff53a1bfa1f5 · outbound

This paper cites OneBit: Towards Extremely Low-bit Large Language Models.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models OneBit: Towards Extremely Low-bit Large Language Models

Reference 12

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:43.341032Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation d39fd99e-8550-427d-bc25-842c1b824fa9 · outbound

This paper cites BiLLM: Pushing the Limit of Post-Training Quantization for LLMs.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models BiLLM: Pushing the Limit of Post-Training Quantization for LLMs

Reference 13

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:43.424174Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c74d2b19-e863-46c6-89a3-37c4cf22ceef · outbound

This paper cites FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models FBI-LLM: Scaling Up Fully Binarized LLMs from Scratch via Autoregressive Distillation

Reference 14

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:43.528232Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 48c5bcc9-ee9b-4ad1-870c-73bfce890510 · outbound

This paper cites ProxQuant: Quantized Neural Networks via Proximal Operators.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models ProxQuant: Quantized Neural Networks via Proximal Operators

Reference 15

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:43.625592Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e5d08d8d-0c72-40e0-bb01-9db0a45e781d · outbound

This paper cites Mirror Descent View for Neural Network Quantization.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models Mirror Descent View for Neural Network Quantization

Reference 16

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:43.747800Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 6f7f2d25-95d6-4df6-a7d3-18d90b9d3ac6 · outbound

This paper cites QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models QuIP#: Even Better LLM Quantization with Hadamard Incoherence and Lattice Codebooks

Reference 17

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:43.851034Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 7f521874-90f9-4496-bf3e-9c4d86c11ac7 · outbound

This paper cites SpinQuant: LLM quantization with learned rotations.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models SpinQuant: LLM quantization with learned rotations

Reference 18

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:43.971644Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation c31bd8a8-aadd-4cbd-9b7c-93a9078cdd3d · outbound

This paper cites Feature Affinity Assisted Knowledge Distillation and Quantization of Deep Neural Networks on Label-Free Data.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models Feature Affinity Assisted Knowledge Distillation and Quantization of Deep Neural Networks on Label-Free Data

Reference 19

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:44.118863Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation e3a42f66-1f49-48d7-bd4a-7db214673753 · outbound

This paper cites ZeroQ: A Novel Zero Shot Quantization Framework.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models ZeroQ: A Novel Zero Shot Quantization Framework

Reference 20

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:44.239669Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation f163b451-23a7-40f2-9979-8ece2f811176 · outbound

This paper cites Distilling the Knowledge in a Neural Network.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models Distilling the Knowledge in a Neural Network

Reference 21

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:44.360434Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3e00ec65-69fb-4f5a-a5a2-5a563f07970f · outbound

This paper cites Kornblith, M.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models Kornblith, M

Reference 22

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:44.479317Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 3bd2d8a6-f640-4e9a-8eac-b50bb628f279 · outbound

This paper cites Qwen2.5 Technical Report.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models Qwen2.5 Technical Report

Reference 23

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:44.623653Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b65a354e-9cbf-4c35-bb57-6a2f31440d4f · outbound

This paper cites Pointer Sentinel Mixture Models.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models Pointer Sentinel Mixture Models

Reference 24

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:44.731000Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 2a8b0c6a-7cab-49b8-b673-dcdbaa84ec16 · outbound

This paper cites Raffel et al.

Cross-Layer Error Compensation and Finite-Sample Feature-Statistics Matching for Extreme Low-Bit Quantization of Large Language Models Raffel et al

Reference 25

Resolution: unresolved; no resolver link, observed 2026-08-02T01:35:44.824860Z

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Unavailable: canonical work link unavailable.

Pith citing papers

No inbound Pith citation observations are available.